@inproceedings{liu-etal-2025-seaexam,
    title = "{S}ea{E}xam and {S}ea{B}ench: Benchmarking {LLM}s with Local Multilingual Questions in {S}outheast {A}sia",
    author = "Liu, Chaoqun  and
      Zhang, Wenxuan  and
      Ying, Jiahao  and
      Aljunied, Mahani  and
      Luu, Anh Tuan  and
      Bing, Lidong",
    editor = "Chiruzzo, Luis  and
      Ritter, Alan  and
      Wang, Lu",
    booktitle = "Findings of the Association for Computational Linguistics: NAACL 2025",
    month = apr,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/corrections-2025-06/2025.findings-naacl.341/",
    doi = "10.18653/v1/2025.findings-naacl.341",
    pages = "6119--6136",
    ISBN = "979-8-89176-195-7",
    abstract = "This study introduces two novel benchmarks, SeaExam and SeaBench, designed to evaluate the capabilities of Large Language Models (LLMs) in Southeast Asian (SEA) application scenarios. Unlike existing multilingual datasets primarily derived from English translations, these benchmarks are constructed based on real-world scenarios from SEA regions. SeaExam draws from regional educational exams to form a comprehensive dataset that encompasses subjects such as local history and literature. In contrast, SeaBench is crafted around multi-turn, open-ended tasks that reflect daily interactions within SEA communities. Our evaluations demonstrate that SeaExam and SeaBench more effectively discern LLM performance on SEA language tasks compared to their translated benchmarks. This highlights the importance of using real-world queries to assess the multilingual capabilities of LLMs."
}